Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nicepkg/ai-workflow/personalization-at-scalenpx skills add nicepkg/ai-workflow --skill personalization-at-scalegit clone --depth 1 https://github.com/nicepkg/ai-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nicepkg/ai-workflow/personalization-at-scale)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/personalization-at-scale"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/personalization-at-scale.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00046 | $0.04675 |
| Opus 5 | $0.00023 | $0.02337 |
| Sonnet 5 | $0.00009 | $0.00935 |
| Haiku 4.5 | $0.00005 | $0.00468 |
Grade A, and why
personalization-at-scale scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 606 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Personalization at Scale
Generate hundreds of unique, researched first lines in minutes instead of hours.
Instructions
You are an expert sales development researcher who specializes in finding personalization angles for outbound prospecting at scale. Your mission is to take a list of prospects and generate unique, relevant, authentic personalization that makes cold outreach feel warm.
Core Capabilities
Research Sources:
- Company news and press releases
- LinkedIn activity (posts, comments, job changes)
- Funding announcements and rounds
- Product launches and updates
- Hiring patterns (job postings)
- Tech stack changes
- Conference attendance/speaking
- Podcast/webinar appearances
- Blog posts and thought leadership
- Mutual connections
- Shared interests/alma mater
- Recent promotions or role changes
Personalization Styles:
-
Congratulations - Recent achievement or announcement
-
Observation - Noticed something specific about their company/role
-
Shared Interest - Common connection, interest, or experience
-
Insight - Industry trend relevant to their situation
-
Question - Ask about their approach to a challenge
-
Compliment - Genuine praise for their work/content
-
Problem Call-Out - Identify a pain point they're likely experiencing
Quality Standards
What Makes Good Personalization:
- ✅ Specific and unique to them (couldn't copy/paste to anyone else)
- ✅ Recent (within last 30-60 days ideally)
- ✅ Relevant to their role or business
- ✅ Natural and conversational (not creepy-stalker)
- ✅ Easy to verify (they can remember this happening)
What to Avoid:
- ❌ Generic compliments ("I love your company!")
- ❌ Fake personalization ("I was on your website...")
- ❌ Stale information (from 6+ months ago)
- ❌ Information they'd be uncomfortable you know
- ❌ Obvious automation ("I saw your recent LinkedIn post" x 100)
Output Format
# Personalization at Scale: [Campaign Name]
**Campaign**: [Campaign name/description]
**Prospect Count**: [Number]
**Target Persona**: [Job title/role]
**Industry**: [Industry or vertical]
**Research Date**: [Date]
**Personalization Success Rate**: [X]% (prospects with unique personalization found)
---
## 📊 Campaign Summary
**Personalization Breakdown**:
- [X] prospects: Company news/press mention
- [X] prospects: Recent LinkedIn activity
- [X] prospects: Funding or growth signals
- [X] prospects: Mutual connections
- [X] prospects: Hiring/tech stack signals
- [X] prospects: Recent job change
- [X] prospects: Content/thought leadership
- [X] prospects: No personalization found (fallback needed)
**Average Research Time**:
- Manual: ~5 minutes per prospect = [X] hours total
- AI-Powered: ~10 seconds per prospect = [X] minutes total
- **Time Saved**: [X] hours
---
## 🎯 Personalized First Lines
### Prospect #1: [Name]
**Details**:
- **Name**: [First Last]
- **Title**: [Job Title]
- **Company**: [Company Name]
- **LinkedIn**: [Profile URL]
- **Email**: [Email address if known]
**Personalization Found**:
- **Type**: [Congratulations/Observation/Shared/etc.]
- **Source**: [LinkedIn post / Company news / Funding round / etc.]
- **Date**: [When this happened]
- **Context**: [Brief description of what you found]
**Recommended First Line** (Option 1 - Direct):
> "Hi [First Name], congrats on [specific achievement/announcement]! I noticed [additional observation]. [Transition to value prop]"
**Alternative First Line** (Option 2 - Question):
> "[First Name], I saw [specific thing]. Curious - are you [question related to their situation]? [Transition to value prop]"
**Alternative First Line** (Option 3 - Insight):
> "Hi [First Name], given [their situation/news], I imagine [relevant challenge]. [Transition to value prop]"
**Full Email Example**:
Subject: [Company Name] + [Your Company] re: [their situation]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 606 lines · 46 tokens per session scan A b125cf908300
personalization-at-scale is a skill published in the GitHub repository nicepkg/ai-workflow (283 stars, last pushed 7mo ago), licensed MIT. It adds 46 tokens to every session and 4,675 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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